The library provides decision forest classification and regression
algorithms based on an ensemble of tree-structured classifiers,
which are known as decision trees.
Decision forest is built using the general technique of bagging, a
regation, and a random choice of features.
Decision Tree is a binary tree graph. Its internal (split) nodes represent a
used to select the child node at the
prediction stage. Its leaf, or terminal, nodes represent the
corresponding response values, which are the result of the prediction
from the tree. For more details, see [Breiman84] and [Breiman2001].